Gene expression analysis of whole blood from preclinical and clinical cattle infected with atypical bovine spongiform encephalopathy

BACKGROUND: Prion diseases, such as bovine spongiform encephalopathies (BSE), are transmissible neurodegenerative disorders affecting humans and a wide variety of mammals. Variant Creutzfeldt-Jakob disease (vCJD), a prion disease in humans, has been linked to exposure to BSE prions. This classical BSE (cBSE) is now rapidly disappearing as a result of appropriate measures to control animal feeding and monitoring. Besides cBSE, two atypical forms (named H- and L-type BSE) have recently been described in Europe, Japan, and North America. Here we describe the first wide-spectrum microarray analysis in whole blood of atypical BSE-infected cattle. Transcriptome changes in infected animals were analyzed prior to and after the onset of clinical signs. Some of the most significant differentially expressed genes (DEGs) were validated by quantitative real time PCR (RT-qPCR). AIM: The aim of this study was to analyze the transcriptome changes in whole blood from atypical BSE-infected animals prior and after the onset of the clinical signs to understand the peripheral mechanisms of prion infection and to line out some candidate genes that could be further investigated as biomarker of the disease. METHODS: Total RNA from whole blood samples from 8 intracranially BSE-challenged cattle (4 with H-type and 4 with L-type BSE) and 2 non-infected age- and sex-matched controls was isolated and subjected to the microarray analysis using the GeneChip\uae Bovine Genome Array (Affymetrix). In order to increase the animal cohort, RNA samples from four additional sex-matched control cattle were isolated using the same protocol as the original study group and included in the final statistical analysis. Therefore, 24 RNA samples, divided in 8 preclinical (P1, P2, P4, P5, P7, P8, EP9 and P10), 8 clinical (S1, S2, S3, S4, S7, S8, S9 and S10), and 8 control (c2, c3, cP3, c5, cS5, cP6, cS6 and c9) samples, constituted our animal cohort. After the assessment and inspection of microarray quality controls (RNA degradation plot, RLE and NUSE plots) we identified one low quality control sample (cS5) and excluded it from the final statistical analysis. Gene probes with a p value 640.05 and fold-change 652 were considered to be differentially expressed. To confirm the microarray results, we performed RT-qPCR using SYBR\uae green assay (Bio-Rad Laboratories, Inc.) for a selected number of target genes. The RT-qPCR analysis was performed on 22 samples (7 control, 8 preclinical and 7 clinical animals). The normalization accuracy was improved by geometric averaging of multiple reference genes (GAPDH, RPL12 and ACTB) and using two inter-run calibrators to reduce inter-run variation. RESULTS: The microarray analysis revealed a total of 101 differentially regulated probe sets (p value lower than 0.05 and changes in expression higher than 2-fold) in infected animals (clinical and preclinical) versus control group. In the clinical stage, a total of 207 probe sets showed significant alteration in expression levels compared to the control group. Interestingly, a pronounced alteration in the gene expression profile was also found in the preclinical stage, with a total number of 113 differentially expressed probe sets. A set of 35 differentially expressed genes was found to be in common between clinical and preclinical stages and showed a very similar expression pattern in the two phases. To further dissect gene expression alterations during the progression of the disease, we performed a statistical analysis to identify specific changes between the clinical and preclinical stages (CvsP). Indeed, we found 235 DEGs, which were significantly enriched in pathways related to immune response. The comparison of all the analysis, revealed a 22-gene signature with an up/down-down/up pattern of expression, being differentially expressed in preclinical stage and then going back to control levels in the symptomatic phase. One gene, SEL1L3, was progressively downregulated during the progression of the disease. The identified genes belong to several pathways, such as immune response and metabolism, that may play an important role in prion pathogenesis. The RT-qPCR analysis confirmed the microarray results for six out of nine genes selected (XIST, CD40L, GNLY, PDK4, HBA2 and SEL1L3). CONCLUSIONS: The present study has led to the identification of several gene expression changes in whole blood from atypical BSE infected cattle prior and after the manifestation of the pathology. Our findings suggest that it might be feasible to use whole blood RNA transcriptional profiles to distinguish between preclinical and clinical stages of prion infection. Overall, our study confirmed the differential expression of 6 genes (XIST, CD40L, GNLY, PDK4, HBA2 and SEL1L3), which may play several roles in atypical BSE pathogenesis and, possibly, in other prion infections. Even though further studies are required to investigate the specific involvement of all the identified genes in prion diseases, our data support the idea of a relationship of complicity and blindness between prion and the host immune system. As concluding remark, our study underlined the importance of utilizing whole blood, without any additional manipulation, as a source tissue for the development of a preclinical diagnostic test

A Role for STOML3 in Olfactory Sensory Transduction

Stomatin-like protein-3 (STOML3) is an integral membrane protein expressed in the cilia of olfactory sensory neurons, but its functional role in this cell type has never been addressed. STOML3 is also expressed in dorsal root ganglia neurons, where it has been shown to be required for normal touch sensation. Here, we extended previous results indicating that STOML3 is mainly expressed in the knob and proximal cilia of olfactory sensory neurons. We additionally showed that mice lacking STOML3 have a morphologically normal olfactory epithelium. Due to its presence in the cilia, together with known olfactory transduction components, we hypothesized that STOML3 could be involved in modulating odorant responses in olfactory sensory neurons. To investigate the functional role of STOML3, we performed loose patch recordings from wild type and Stoml3 KO olfactory sensory neurons. We found that spontaneous mean firing activity was lower with additional shift in interspike intervals distributions in Stoml3 KOs compared to wild type neurons. Moreover, the firing activity in response to stimuli was reduced both in spike number and duration in neurons lacking STOML3 compared to wildtype neurons. Control experiments suggested that the primary deficit in neurons lacking STOML3 was at the level of transduction and not at the level of action potential generation. We conclude that STOML3 has a physiological role in olfaction, being required for normal sensory encoding by olfactory sensory neurons.Significance Statement Olfactory transduction comprises a series of well-characterized molecular steps that take place in the cilia of olfactory sensory neurons (OSNs) terminating in action potential firing. Here, we introduce a possible new player: stomatin-like protein 3 (STOML3). Indeed, STOML3 is localized in olfactory cilia, and we show that STOML3 plays a role in OSN physiology. First, it allows OSNs to broaden the possible frequency range of their spontaneous activity. Second, STOML3 modulates odorant-evoked action potential firing by regulating both the number of spikes and response duration. These new findings call for a reconsideration of the patterns of the peripheral coding of sensory stimuli

Whole blood gene expression profiling in preclinical and clinical cattle infected with atypical bovine spongiform encephalopathy

Prion diseases, such as bovine spongiform encephalopathies (BSE), are transmissible neurodegenerative disorders affecting humans and a wide variety of mammals. Variant Creutzfeldt-Jakob disease (vCJD), a prion disease in humans, has been linked to exposure to BSE prions. This classical BSE (cBSE) is now rapidly disappearing as a result of appropriate measures to control animal feeding. Besides cBSE, two atypical forms (named Hand L-type BSE) have recently been described in Europe, Japan, and North America. Here we describe the first wide-spectrum microarray analysis in whole blood of atypical BSEinfected cattle. Transcriptome changes in infected animals were analyzed prior to and after the onset of clinical signs. The microarray analysis revealed gene expression changes in blood prior to the appearance of the clinical signs and during the progression of the disease. A set of 32 differentially expressed genes was found to be in common between clinical and preclinical stages and showed a very similar expression pattern in the two phases. A 22-gene signature showed an oscillating pattern of expression, being differentially expressed in the preclinical stage and then going back to control levels in the symptomatic phase. One gene, SEL1L3, was downregulated during the progression of the disease. Most of the studies performed up to date utilized various tissues, which are not suitable for a rapid analysis of infected animals and patients. Our findings suggest the intriguing possibility to take advantage of whole blood RNA transcriptional profiling for the preclinical identification of prion infection. Further, this study highlighted several pathways, such as immune response and metabolism that may play an important role in peripheral prion pathogenesis. Finally, the gene expression changes identified in the present study may be further investigated as a fingerprint for monitoring the progression of disease and for developing targeted therapeutic interventions. \ua9 2016 Xerxa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data sets of human and mouse protein kinase inhibitors with curated activity data including covalent inhibitors

Aim: Generation of high-quality data sets of protein kinase inhibitors (PKIs). Methodology: Publicly available PKIs with reliable activity data were curated. PKIs with very weak activity were classified as inactive. Analogue series and PKIs containing reactive groups (warheads) enabling covalent inhibition were systematically identified. Exemplary results & data: A total of 155,579 human and 3057 mouse PKIs were obtained. Human PKIs were active 440 kinases and included 13,949 covalent PKIs. The collection of qualifying PKIs and corresponding inactive compounds is made available as an open access deposition. Limitations & next steps: Potential limitations include activity data incompleteness and assay variance. The data set can be used to investigate PKIs with alternative modes of action and calibrate computational methods

Comprehensive Data-Driven Assessment of Non-Kinase Targets of Inhibitors of the Human Kinome

Protein kinases (PKs) are involved in many intracellular signal transduction pathways through phosphorylation cascades and have become intensely investigated pharmaceutical targets over the past two decades. Inhibition of PKs using small-molecular inhibitors is a premier strategy for the treatment of diseases in different therapeutic areas that are caused by uncontrolled PK-mediated phosphorylation and aberrant signaling. Most PK inhibitors (PKIs) are directed against the ATP cofactor binding site that is largely conserved across the human kinome comprising 518 wild-type PKs (and many mutant forms). Hence, these PKIs often have varying degrees of multi-PK activity (promiscuity) that is also influenced by factors such as single-site mutations in the cofactor binding region, compound binding kinetics, and residence times. The promiscuity of PKIs is often—but not always—critically important for therapeutic efficacy through polypharmacology. Various in vitro and in vivo studies have also indicated that PKIs have the potential of interacting with additional targets other than PKs, and different secondary cellular targets of individual PKIs have been identified on a case-by-case basis. Given the strong interest in PKs as drug targets, a wealth of PKIs from medicinal chemistry and their activity data from many assays and biological screens have become publicly available over the years. On the basis of these data, for the first time, we conducted a systematic search for non-PK targets of PKIs across the human kinome. Starting from a pool of more than 155,000 curated human PKIs, our large-scale analysis confirmed secondary targets from diverse protein classes for 447 PKIs on the basis of high-confidence activity data. These PKIs were active against 390 human PKs, covering all kinase groups of the kinome and 210 non-PK targets, which included other popular pharmaceutical targets as well as currently unclassified proteins. The target distribution and promiscuity of the 447 PKIs were determined, and different interaction profiles with PK and non-PK targets were identified. As a part of our study, the collection of PKIs with activity against non-PK targets and the associated information are made freely available

Heat maps representing the DEGs found in clinical and preclinical cattle with atypical BSE.

<p>Two heat maps were generated using the <i>heatmap</i>.<i>2</i> function from the <i>gplots</i> library in <i>R</i> statistical environment. DEGs were hierarchically clustered with complete linkage using the Euclidean metric. The heat maps represent the most significant DEGs (p value ≤.0.05 and fold change ≥ 2) in clinical (A) and preclinical (B) animals compared to the control group. Animals are reported in the x-axis while the differentially expressed probes are in the y-axis.</p

Differential expression of selected genes quantified by microarray and RT-qPCR analysis ^a.

<p>Differential expression of selected genes quantified by microarray and RT-qPCR analysis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153425#t005fn001" target="_blank">^a</a>.</p

Gene enrichment analysis of DEGs specific of the clinical and preclinical stage of the disease.

<p>The most relevant GO terms (y axis) associated to clinical (<b>A</b>) and preclinical (<b>B</b>) phase are listed according to decreasing statistical significance from top to bottom. The threshold for statistical significance is marked by the green lines. The enrichment analysis was performed using DAVID bioinformatics tool 6.7 (NIAID/NIH, USA).</p

Functional classification of differentially expressed genes in blood of infected cattle versus control group^a.

<p>Functional classification of differentially expressed genes in blood of infected cattle versus control group<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153425#t002fn001" target="_blank">^a</a>.</p

Identification of common DEGs in blood of preclinical and clinical atypical BSE-infected cattle.

<p>(A) Venn diagram showing the number of differentially expressed probe sets in blood of clinical and preclinical cattle. The intersection in grey represents 35 differentially expressed probe sets corresponding to 32 differentially expressed genes (DEGs) that are in common between the two stages of the disease. (B) Expression pattern of the common 32 DEGs. The histograms represent the fold change relative to the control group. PvsCtrl = preclinical versus control, CvsCtrl = clinical versus control.</p